Footprints of antigen processing boost MHC class II natural ligand predictions

TY - JOUR

T1 - Footprints of antigen processing boost MHC class II natural ligand predictions

AU - Barra, Carolina

AU - Alvarez, Bruno

AU - Paul, Sinu

AU - Sette, Alessandro

AU - Peters, Bjoern

AU - Andreatta, Massimo

AU - Buus, Søren

AU - Nielsen, Morten

PY - 2018

Y1 - 2018

N2 - BACKGROUND: Major histocompatibility complex class II (MHC-II) molecules present peptide fragments to T cells for immune recognition. Current predictors for peptide to MHC-II binding are trained on binding affinity data, generated in vitro and therefore lacking information about antigen processing. METHODS: We generate prediction models of peptide to MHC-II binding trained with naturally eluted ligands derived from mass spectrometry in addition to peptide binding affinity data sets. RESULTS: We show that integrated prediction models incorporate identifiable rules of antigen processing. In fact, we observed detectable signals of protease cleavage at defined positions of the ligands. We also hypothesize a role of the length of the terminal ligand protrusions for trimming the peptide to the MHC presented ligand. CONCLUSIONS: The results of integrating binding affinity and eluted ligand data in a combined model demonstrate improved performance for the prediction of MHC-II ligands and T cell epitopes and foreshadow a new generation of improved peptide to MHC-II prediction tools accounting for the plurality of factors that determine natural presentation of antigens.

AB - BACKGROUND: Major histocompatibility complex class II (MHC-II) molecules present peptide fragments to T cells for immune recognition. Current predictors for peptide to MHC-II binding are trained on binding affinity data, generated in vitro and therefore lacking information about antigen processing. METHODS: We generate prediction models of peptide to MHC-II binding trained with naturally eluted ligands derived from mass spectrometry in addition to peptide binding affinity data sets. RESULTS: We show that integrated prediction models incorporate identifiable rules of antigen processing. In fact, we observed detectable signals of protease cleavage at defined positions of the ligands. We also hypothesize a role of the length of the terminal ligand protrusions for trimming the peptide to the MHC presented ligand. CONCLUSIONS: The results of integrating binding affinity and eluted ligand data in a combined model demonstrate improved performance for the prediction of MHC-II ligands and T cell epitopes and foreshadow a new generation of improved peptide to MHC-II prediction tools accounting for the plurality of factors that determine natural presentation of antigens.

KW - Antigen processing

KW - Binding predictions

KW - Eluted ligands

KW - Machine learning

KW - Mass spectrometry

KW - MHC-II

KW - Neural networks

KW - T cell epitope

U2 - 10.1186/s13073-018-0594-6

DO - 10.1186/s13073-018-0594-6

M3 - Journal article

C2 - 30446001

AN - SCOPUS:85056738224

SN - 1756-994X

VL - 10

JO - Genome Medicine

JF - Genome Medicine

M1 - 84

ER -